Hard handoff minimization using genetic algorithms

Chan, T.M.; Kwong, S.; Man, K.F.; Tang, Wallace K. S.

doi:10.1016/s0165-1684(02)00213-x

Cited by 14 publications

(3 citation statements)

References 21 publications

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“…Evolutionary theory and GAs have been studied for many areas in telecommunications and for cluster formation and optimisation problems [18,19]. In Reference [20], a GA is applied to wireless sensor networks for energy efficient clustering; GA solutions for mobile ad hoc networks are presented in References [21] and [22], whereas GAs applied to handoff problems in cellular networks are addressed in Reference [23]. A GA has also been proposed to approach a routing and scheduling problem in a mesh environment in Reference [24].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Network coding and evolutionary theory for performance enhancement in wireless cooperative clusters

Militano

Fitzek

Molinaro

2010

Trans Emerging Tel Tech

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SUMMARYCooperation over short-range wireless links among user devices, which download remote contents through cellular links, is a paradigm quickly gaining ground, given that it can answer several technological and design issues that next-generation wireless applications will raise. Among others, energy consumption, data rate and data transfer time are parameters that will likely benefit from this novel communication concept. This paper presents a WLAN-based cooperative scenario, conceived to support file-sharing services, in which a sub-set of cluster nodes access, through their cellular radio interface, portions of a file to be successively exchanged among all cluster members over wireless local area network (WLAN) links. Besides showing the beneficial effects of cooperation, this paper also focuses on the performance enhancement that can be achieved when using the network coding paradigm, whose deployment revealed to be very effective in the depicted scenario. The selection of the nodes acting as sources for file injection into the cluster is a critical aspect for the effectiveness of the whole system. Therefore, the potentialities offered by the evolutionary theory applied to the specific problem are investigated and an ad hoc conceived Genetic Algorithm (GA) designed. Either the service time (the time needed for all nodes to receive the complete file) or the energy consumption for the nodes is used as objective function, showing in both cases the fast convergence for the algorithm that makes it preferable to either exhaustive searches or random choices.

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Section: Genetic Algorithmsmentioning

confidence: 99%

Network coding and evolutionary theory for performance enhancement in wireless cooperative clusters

Militano

Fitzek

Molinaro

2010

Trans Emerging Tel Tech

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“…Several heuristics were proposed to solve the LAP: Tabu Search (TS) algorithms [3], Greedy Search (GS) algorithms [13], Simulated Annealing (SA) [4] [5], Genetic Algorithms (GA) [15] [2] and Gondim (1996) [6], and Grouping GAs (GGA) [15] [14]. We can notice that most researches reported in the literature focused only on the uni-objective LAP.…”

Section: Introductionmentioning

confidence: 99%

A genetic algorithm for the two-level location area planning in telecommunication networks

Abdelkhalek¹,

Krichen

Guitouni

2013

2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)

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With the growing number of mobile users, the management of wireless networks to expand network capacity is of increasing importance. The Location Areas Planning (LAP) problem examines the redistribution of network resources to prevent any degradation in the quality of service. The optimization of these resources minimizes the cost of the registration signaling generated by the procedures of mobility. This paper introduces a new formulation of the "Bi-objective Location Area-Planning" (BOLAP) problem. Modeled as a Two-level assignment problem, we minimize two objectives successively: the location Update (LU) then the cost of the BTS-BSC links connexions. The model is then iterated until no more improvement is performed in the set of optimal solutions.We propose to adopt a Genetic Algorithm (GA) to solve the Two-level BOLAP model. Applied on a sample of real instances with different sizes for a big Tunisian telephony operator, our method generates a set of potentially efficient solutions of a good quality in a practicable CPU time. A comparison with the vector evaluated particle swarm optimization (VEPSO) is also reported.

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“…The problem (3-11) -(3-12) belongs to the class of constrained optimization which happens in many applications [71][72]. This class of problem is often transformed into the unconstrained type that is easier to solve.…”

Section: Popular Nonlinear Optimization Approachesmentioning

confidence: 99%

Received signal strength based localization of a wireless sensor network using particle swarm optimization

Nguyen¹

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Chapter 1 Introduction 1 1.1 Wireless sensor networks and applications 1 1.2 Overview of localization techniques 2 1.3 Motivation 4 1.4 Objectives 7 1.5 Main contributions 8 1.6 Organization of thesis 9 Chapter 2 Literature review of localization techniques for wireless sensor networks 12 2.1 Introduction 12 2.2 Range-free localization 13 2.3 Range-based localization 17 2.3.1 Time-Of-Arrival (TOA) 17 2.3.2 Angle-Of-Arrival (AOA) 18 2.3.3 Received Signal Strength (RSS) 20 2.3.4 Triangulation, trilateration and maximum likelihood 23 2.4 Other localization techniques 24 2.4.1 Kalman filter 25 2.4.2 Monte Carlo localization 26 2.4.3 Multi-Dimensional Scaling (MDS) 27 2.5 Conclusion 28 Chapter 3 Real-time localization using Particle Swarm Optimization with logbarrier constraint 29 3.1 Introduction 29 3.2 System model 30 3.2.1 Communication path loss modeling 31 iii ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library 3.3 Problem formulation 32 3.4 Constrained nonlinear optimization using log-barrier method 35 3.4.1 Popular nonlinear optimization approaches 35 3.4.2 Optimization with log-barrier approach 39 3.4.3 Particle swarm optimization 3.5 Test case 3.5.1 Standard PSO versus PSO with classical log-barrier 3.5.2 Modified barrier function versus classical barrier function 3.6 Conclusion Chapter 4 Performance analysis and parameters tuning of the proposed algorithm 4.1 Introduction 4.2 PSO convergence 4.3 Effect of PSO parameters on localization performance 56 4.4 Localization performance under different RSSI standard deviations 61 4.5 Effect of barrier parameter on localization accuracy 64 4.6 Comparison between PSO-log barrier and memetic algorithms 66 4.7 Performance comparison 71 4.8 Conclusion 76

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Hard handoff minimization using genetic algorithms

Cited by 14 publications

References 21 publications

Network coding and evolutionary theory for performance enhancement in wireless cooperative clusters

Network coding and evolutionary theory for performance enhancement in wireless cooperative clusters

A genetic algorithm for the two-level location area planning in telecommunication networks

Received signal strength based localization of a wireless sensor network using particle swarm optimization

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